Metadata-Version: 2.4
Name: learnsaxs
Version: 0.0.4
Summary: Jupyter Notebook examples for SEC-SAXS learning
Project-URL: Repository, https://github.com/freesemt/learnsaxs
Author-email: Masatsuyo Takahashi <freesemt@gmail.com>
Maintainer-email: Masatsuyo Takahashi <freesemt@gmail.com>
License: MIT License
        
        Copyright (c) 2021 freesemt
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Keywords: SEC-SAXS
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python
Requires-Python: >=3.9
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: scipy
Description-Content-Type: text/markdown

# learnsaxs
This project includes several notebooks and a small library to help you learn SAXS with Python in Jupyter,
which cover the following subjects:
  * viewing electron density voxels of an ellipsoid in 3D real space
  * Fourier transform of the voxel values into the reciprocal space
  * spherically averaging in the reciprocal space
  * which produces a detector image and a scattering curve
as shown in the figure below.

<img src="images/detector.png">

The notebooks are designed to use a minimum number of libraries, i.e.,

  * numpy
  * matplotlib
  * learnsaxs

the last of which, "learnsaxs", is provided here to include following few functions

  * draw_voxles_as_dots
  * get_detector_info
  * draw_detector_image

to make the notebook examples as concise as possible.

You can install the learnsaxs package using pip command as follows.

```
pip install learnsaxs
```

Hoping this may be of any help.
